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Record W3088943958 · doi:10.1177/0959680120951705

Unions and the green transition in construction in Europe: Contrasting visions

2020· article· en· W3088943958 on OpenAlexfundno aff
Linda Clarke, M. Sahin-Dikmen

Bibliographic record

VenueEuropean Journal of Industrial Relations · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
FundersYork University
KeywordsDirectiveVisionAcknowledgementEuropean unionVocational educationModernization theoryGreen growthEfficient energy useBusinessBrexitSocial dialogueEconomic systemPolitical scienceEconomicsEconomic growthLabour economicsEconomic policySociologySustainable developmentEngineering

Abstract

fetched live from OpenAlex

The construction industry, responsible for 40 percent of European Union (EU) end-use emissions, is targeted as a major area of transformation particularly through the Energy Performance of Buildings Directive requiring nearly zero energy building (NZEB). Through a case study approach, union responses to EU strategy on the implementation of energy efficiency standards are evaluated in Denmark, Germany, Italy and UK (Scotland), presenting a varied picture, from minimal acknowledgement to broad support along the lines of ecological modernization to radical transformation. Radical appraisals of the industry and its exploitative and high-carbon practices are rare, though engaging with the employment and vocational education and training (VET) implications. The article presents a labour-centred alternative to a technical-driven transition agenda, focusing on how the labour process needs to change in a sector dominated by small firms, self-employment, a fragmented labour process and often low levels of VET.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0100.026
Scholarly communication0.0180.005
Open science0.0010.010
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.261
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations38
Published2020
Admission routes1
Has abstractyes

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